Prediction of COVID-19 new cases per day will help the administration and planners to take the proper decision and help them in making effective policy to tackle the pandemic situation. The epidemiological models are very helpful to understand the trend of COVID-19 spread and useful in predicting the spread rate of the disease, the duration of the disease, and the peak of the infectious disease. It can be used for short term and long term predictions for new confirmed COVID-19 cases per day that may be used in decision making to optimize possible controls from the infectious disease. In literature, several mathematical models for infectious diseases such Logistic models [1]}, generalized growth models [2]}, Richards’s models [3]}, sub epidemics wave models [4]}, Susceptible-Infected-Recovered (SIR) model [5]}, and Susceptible-Exposed-Infectious-Removed (SEIR) have been introduced. The SIR model is a compartmental model that considers the whole population as a closed population and divides this closed population into susceptible, infected, and recovered compartments. Few infected persons infect some other persons at an average rate \( R0 \) , known as the basic reproduction number. Recently, some works have been reported in the literature using the SIR and its variants model to predict the COVID-19 outbreak [6]}, [7]}, [8]}, [9]}, [10]}. These epidemiological models are good in understanding the trend of COVID-19 spread but are designed based on several assumptions that would not hold generally on real-life data[11]}. It is unreliable due to the complex trend of spread of the infection as it depends on population density, travel, and individual social aspects like cultural and life styles. Therefore, there is a need for deep learning approaches to accurately predict the COVID-19 trends in India.
In deep learning, convolutional neural network (CNN) [12]} is one form of deep learning architecture for processing data that has a grid like topology. It includes the time series data that can be considered as 1D grid taking samples at regular time intervals and image data considered as 2D grid of pixels. A typical end-to-end CNN network consists of different layers such as convolution, activation, max-pooling, softmax layer etc.
